Probabilistic information on object weight shapes force dynamics in a grip-lift task
Advance information, such as object weight, size and texture, modifies predictive scaling of grip forces in a grip-lift task. Here, we examined the influence of probabilistic advance information about object weight. Fifteen healthy volunteers repeatedly grasped and lifted an object equipped with a force transducer between their thumb and index finger. Three clearly distinguishable object weights were used. Prior to each lift, the probabilities for the three object weights were given by a visual cue. We examined the effect of probabilistic pre-cues on grip and lift force dynamics. We expected predictive scaling of grip force parameters to follow predicted values calculated according to probabilistic contingencies of the cues. We observed that probabilistic cues systematically influenced peak grip and load force rates, as an index of predictive motor scaling. However, the effects of probabilistic cues on force rates were nonlinear, and anticipatory adaptations of the motor output generally seemed to overestimate high probabilities and underestimate low probabilities. These findings support the suggestion that anticipatory adaptations and force scaling of the motor system can integrate probabilistic information. However, probabilistic information seems to influence motor programs in a nonlinear fashion.
KeywordsAdaptation Anticipation Motor system Motor load Force rate Predictive
We would like to thank Gesa Hartwigsen for her input regarding the experimental setup and Martin Müller for technical advice and for constructing the lever.
- Eastough D, Edwards MG (2007) Movement kinematics in prehension are affected by grasping objects of different mass. Exp Brain Res 10(6):193–198Google Scholar
- Johansson RS, Flanagan R (2009) Sensory control of object manipulation. In: Nowak D, Hermsdörfer J (eds) Sensorimotor control of grasping. Cambridge University Press, Cambridge, pp 141–160Google Scholar
- R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org
- Stevens SS (1971) Sensory power functions and neural events. In: Autrum H, Jung R, Loewenstein WR, MacKay DM, Teuber HL (eds) Handbook of sensory physiology, vol 1, 1st edn. Springer, Heidelberg, pp 226–242Google Scholar